DocumentCode
1426265
Title
Optimum detection and segmentation of oil-slicks using polarimetric SAR data
Author
Lombardo, P. ; Oliver, C.J.
Author_Institution
INFOCOM Dept., Rome Univ., Italy
Volume
147
Issue
6
fYear
2000
fDate
12/1/2000 12:00:00 AM
Firstpage
309
Lastpage
321
Abstract
The paper is concerned with techniques for optimising the detection and definition of slick boundaries on the sea surface using polarimetric imagery. In principle, the full polarimetric return should provide more information than is available in a single polarisation. The authors compare the performance of a set of different polarisation measures applied to the detection of slicks. Annealed segmentation of these measures is then employed to detect and define their boundaries. Theoretical predictions are derived for the probability of detection using conventional polarisation measures, including the intensity in a single polarisation and the maximum eigenvalue and span measures for more than one polarisation channel. The authors also propose two implementations of a maximum likelihood polarisation discriminant and demonstrate that these yield significant improvement in slick detection and boundary definition
Keywords
image segmentation; maximum likelihood detection; oceanographic techniques; pollution measurement; probability; radar detection; radar imaging; radar polarimetry; remote sensing by radar; simulated annealing; synthetic aperture radar; detection probability; maximum eigenvalue; maximum likelihood polarisation discriminant; oil slick boundary definition; optimum detection; optimum oil slick detection; optimum oil slick segmentation; polarimetric SAR data; polarimetric imagery; polarimetric return; polarisation channel; polarisation intensity; polarisation measures performance; remote sensing; sea surface; simulated annealed segmentation; span measures;
fLanguage
English
Journal_Title
Radar, Sonar and Navigation, IEE Proceedings -
Publisher
iet
ISSN
1350-2395
Type
jour
DOI
10.1049/ip-rsn:20000557
Filename
895814
Link To Document